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a b s t r a c t Delineation of stock structure is important for understanding the ecology and management of many fish populations, particularly those with wide-ranging distributions and high levels of harvest. Southern flounder (Paralichthys lethostigma) is a popular commercial and recreational species along the southeast Atlantic coast and Gulf of Mexico, USA. Recent studies have provided genetic and otolith morphology evi- dence that the Gulf of Mexico and Atlantic Ocean stocks differ. Using age and growth data from four states (Texas, Alabama, South Carolina, and North Carolina) we expanded upon the traditional von Bertalanffy model in order to compare growth rates of putative geographic stocks of southern flounder. We improved the model fitting process by adding a hierarchical Bayesian framework to allow each parameter to vary spatially or temporally as a random effect, as well as log transforming the three model parameters (L∞, K, and t0). Multiple comparisons of parameters showed that growth rates varied (even within states) for females, but less for males. Growth rates were also consistent through time, when long-term data were available. Since within-basin populations are thought to be genetically well-mixed, our results suggest that consistent small-scale environmental conditions (i.e., within estuaries) likely drive growth rates and should be considered when developing broader scale management plans.

This study investigated the contribution of shrimp stocks in supporting the production of valuable predator species. Fishery-independent data on white shrimp, brown shrimp, and selected fish species (spotted seatrout, red drum, and southern flounder) were collected from 1986 to 2014 by the Texas Parks and Wildlife Department, and converted to catch-per-unit effort (CPUE). Here, the associations between the CPUEs of fish species as predators and those of shrimp species as prey in each sampled bay and sampling season were analyzed using co-integration analysis and Partial Least Squares Regression (PLSR). Co-integration analysis revealed significant associations between 31 of 70 possible fish/shrimp pairings. The analysis also revealed discernible seasonal and spatial patterns. White shrimp in August and brown shrimp in May were associated with fish CPUEs in bays located along the lower coast of Texas, whereas white shrimp in November was more strongly associated with fish CPUEs in bays located on the upper coast. This suggests the possible influence of latitudinal environmental gradient. The results of the PLSR, on the other hand, were not conclusive. This may reflect the high statistical error rates inherent to the analysis of short non-stationary time series. Co-integration is a robust method when analyzing non-stationary time series, and a majority of time series in this study was non-stationary. Based on our co-integration results, we conclude that the CPUE data show significant associations between shrimp abundance and the three predator fish species in the tested regions. Published version